DeepMind
Artificial Intelligence beyond AlphaGo
DeepMind Technologies Limited is a British company now owned by Google. DeepMind is perhaps best known for AlphaGo, its AI-based software that plays the game of Go, evidently at least to a world championship standard. However, DeepMind's AI patenting activities indicate other interests going beyond board games and covering various fields of application of AI.
DeepMind Patent Application Families
Up to the present time1 , members of around 58 DeepMind families2 of patent applications have been published. Titles and application/patent numbers of key members3 of each family are shown in Table 4 below. A total of 85 members of the families have been published up to the present time1. Members of 4 families were published in 2014, having filing dates in 2012, 2013, and 2014. However, members of the other families are more recent, being published in 2018 and 2019, with application dates from 2016 to 2018. It is to be expected that more families and family members will appear as further publications take place.
DeepMind Patents
As would be expected with such a young portfolio, few patents have been granted so far. These are listed in Table 1. Claim 1 of each of the US patents is reproduced further below. So far, there are no granted European patents in the published record1.
Table 1 |
|
DeepMind Patents |
|
CN105144203(B) |
Signal processing systems |
US9342781(B2) |
|
US10032089(B2) |
Spatial transformer |
modules |
|
US10176424(B2) |
Generative neural |
networks |
|
US10198832(B2) |
Generalizable medical |
image analysis using |
|
segmentation and |
|
classification neural |
|
networks |
|
US8644607(B1) |
Method and apparatus for |
US8971669(B2) |
image processing |
Fields of Interest for DeepMind
Although DeepMind's interests go beyond board games, they appear to be focused on a narrow range of technologies, which is indicated by detailed classifications of the members of the DeepMind portfolio under the Cooperative Patent Classification (CPC) hierarchy1 .
Each patent or application is usually given more than one detailed classification to main groups or sub-groups of the CPC. DeepMind's families have been given 235 detailed classifications, all falling within only 9 CPC sub-classes, as shown in Table 2. The vast majority of detailed classifications fall within sub-class G06N, which covers – amongst other things – neural networks. Seen in more detail, the 235 classifications range across only 65 CPC main/sub-groups, with 80% of those classifications accounted for by 17 main/sub-groups as shown in Table 3. These focus on neural networks, machine learning, and the context of natural language processing, image analysis, speech synthesis etc., with a surprise mention of musical instruments.
Table 2 |
||
CPC sub-class |
No. of detailed classifications |
CPC description |
within sub-class |
||
G06N |
187 |
Computer systems based on specific computational models |
G06K |
18 |
Recognition of data; presentation of data; record carriers; |
handling record carriers |
||
G06F |
11 |
Electric digital data processing |
G06T |
8 |
Image data processing or generation, in general |
G10L |
5 |
Speech analysis or synthesis; speech recognition; speech or |
voice processing; speech or audio coding or decoding |
||
G10H |
2 |
Electrophonic musical instruments |
H04N |
2 |
Pictorial communication, e.g. television |
G05B |
1 |
Control or regulating systems in general; functional elements |
of such systems; monitoring or testing arrangements for such |
||
systems or elements |
||
Y04S |
1 |
Systems integrating technologies related to power network |
operation, communication or information technologies for |
||
improving the electrical power generation, transmission, |
||
distribution, management or usage, i.e. smart grids |
Table 3 |
||
CPC main group/ |
No. of detailed classifications |
CPC description |
sub-group |
to main group/sub-group |
|
G06N3/0454 |
38 |
Computer systems based on biological models; Architectures, |
e.g. interconnection topology; using a combination of multiple |
||
neural nets |
||
G06N3/08 |
31 |
Computer systems based on biological models; Learning |
methods |
||
G06N3/04 |
22 |
Computer systems based on biological models; Architectures, |
e.g. interconnection topology |
||
G06N3/0445 |
22 |
Computer systems based on biological models; Architectures, |
e.g. interconnection topology; Feedback networks, e.g. |
||
hopfield nets, associative networks |
||
G06N3/006 |
17 |
Computer systems based on biological models; Physical |
realisation, i.e. hardware implementation of neural networks, |
||
neurons or parts of neurons |
||
G06N3/084 |
13 |
Computer systems based on biological models; Learning |
methods; Back-propagation |
||
G06N3/088 |
10 |
Computer systems based on biological models; Learning |
methods; Non-supervised learning, e.g. competitive learning |
||
G06N3/0472 |
9 |
Computer systems based on biological models; Architectures, |
e.g. interconnection topology; using probabilistic elements, |
||
e.g. p-rams, stochastic processors |
||
G06N3/00 |
8 |
Computer systems based on biological models |
G06N3/063 |
3 |
Computer systems based on biological models; Physical |
realisation, i.e. hardware implementation of neural networks, |
||
neurons or parts of neurons |
||
G06F17/2818 |
2 |
Digital computing or data processing equipment or methods, |
specially adapted for specific functions; Processing or |
||
translating of natural language; Statistical methods, e.g. |
||
probability models |
||
G06K9/4628 |
2 |
Methods or arrangements for reading or recognising printed |
or written characters or for recognising patterns, e.g. |
||
fingerprints; Extraction of features or characteristics of the |
||
image; integrating the filters into a hierarchical structure |
||
G06N3/02 |
2 |
Computer systems based on biological models; using neural |
network models |
||
G06N3/0481 |
2 |
Computer systems based on biological models; Architectures, |
e.g. interconnection topology; Non-linear activation functions, |
||
e.g. sigmoids, thresholds |
||
G06N3/082 |
2 |
Computer systems based on biological models; Learning |
methods; modifying the architecture, e.g. adding or deleting |
||
nodes or connections, pruning |
||
G10H2250/311 |
2 |
Aspects of algorithms or signal processing methods without |
intrinsic musical character, yet specifically adapted for or used |
||
in electrophonic musical processing; Neural networks for |
||
electrophonic musical instruments or musical processing, e.g. |
||
for musical recognition or control, automatic composition or |
||
improvisation |
||
G10L13/00 |
2 |
Speech synthesis; Text to speech systems |
DeepMind Patent US9342781 (B2)
- A neural network system implemented as one or more computers for generating samples of a particular sample type, wherein each generated sample belongs to a respective category of a predetermined set of categories, and wherein each generated sample is an ordered collection of values, each value having
a sample position in the collection, and wherein the system comprises:
a first stochastic layer configured to stochastically select a category from the predetermined set of categories;
a first deterministic subnetwork configured to: receive an embedding vector corresponding to the selected category, and
process the embedding vector to generate a respective sample score for each sample position in the collection; and
a second stochastic layer configured to generate an output sample by stochastically selecting, for each sample position, a sample value using the sample score for the sample position.
DeepMind Patent US10032089 (B2)
- An image processing neural network system implemented by one or more computers, wherein the image processing neural network system is configured to receive one or more input images and to process the one or more input images to generate a neural network output from the one or more input images, the image processing neural network system comprising:
a spatial transformer module, wherein the spatial transformer module is configured to perform operations comprising:
receiving an input feature map derived from the one or more input images, and
applying a spatial transformation to the input feature map to generate a transformed feature map, comprising:
processing the input feature map to generate, based on the input feature map, spatial transformation parameters that define the spatial transformation to be applied to the input feature map, and
sampling from the input feature map in accordance with the spatial transformation parameters generated based on the input feature map to generate the transformed feature map.
DeepMind Patent US10176424 (B2)
- A neural network system implemented by one or more computers, the neural network system comprising:
a recurrent neural network that is configured to, for each time step of a predetermined number of time steps, receiv e a set of latent variables for the time step and process the set of latent variables to update a hidden state of the recurrent neural network; and
a generative subsystem that is configured to:
for each time step of the predetermined number of time steps:
generate the set of latent variables for the time step and provide the set of latent variables as input to the recurrent neural network;
update a hidden canvas using the updated hidden state of the recurrent neural network; and
for a last time step of the predetermined number of time steps:
generate an output image using the updated hidden canvas for the last time step.
DeepMind Patent US10198832 (B2)
- A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement:
a first set of one or more segmentation neural networks, wherein each segmentation neural network in the first set is configured to:
receive an input image of eye tissue captured using a first imaging modality; and
process the input image to generate a segmentation map that segments the eye tissue in the input image into a plurality of tissue types;
a set of one or more classification neural networks, wherein each classification neural network is configured to:
receive a classification input derived from a segmentation map of eye tissue; and
process the classification input to generate a classification output that characterizes the eye tissue; and a subsystem configured to:
receive a first image of eye tissue captured using the first imaging modality;
provide the first image as input to each of the segmentation neural networks in the first set to obtain one or more segmentation maps of the eye tissue in the first image;
generate, from each of the segmentation maps, a respective classification input; and
provide, for each of the segmentation maps, the classification input for the segmentation map as input to each of the classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network; and
generate, from the respective classification outputs for each of the segmentation maps, a final classification output for the first image.
DeepMind Patent US8644607 (B1)
- A method for processing an image to generate a signature which is characteristic of a pattern within said image comprising:
receiving an image;
overlaying a window at multiple locations on said image to define a plurality of sub-images within said image, with each sub-image each having a plurality of pixels having a luminance level;
determining a luminance value for each said sub-image, wherein said luminance value is derived from said luminance levels of said plurality of pixels;
combining said luminance values for each of said sub-images to form said signature;
wherein said combining is such that said signature is independent of the location of each sub-image.
DeepMind Patent US8971669 (B2)
- A non-transitory computer readable medium storing a computer program code that, when executed by one or more computers, causes the one or more computers to perform operations for processing an image to generate a signature which is characteristic of a pattern within the image, the operations comprising:
receiving an image;
overlaying a window at multiple locations on the image to define a plurality of sub-images within the image, with sub-image having a plurality of pixels having a luminance level;
determining a luminance value for each sub-image, wherein said luminance value is derived from the luminance levels of the plurality of pixels in the sub-image; and
combining the luminance values for each of the sub-images to form a signature for the image;
wherein the combining is such that the signature is independent of the location of each sub-image.
Table 4 |
|
Title |
Publication number |
Method And Apparatus For Image Searching |
US2014019484 (A1) |
Method And Apparatus For Conducting A Search |
US2014019431 (A1) |
Method And Apparatus For Image Processing |
US2014185959 (A1); |
US8971669 (B2) |
|
Signal Processing Systems |
GB2513105 (A) |
Generative Neural Networks |
US10176424 (B2); |
US2017228633 (A1) |
|
Umgebungsnavigation Unter Verwendung Von Verstärkungslernen |
DE202017106697 (U1) |
Processing Sequences Using Convolutional Neural Networks |
WO2018048945 (A1) |
Generating Video Frames Using Neural Networks |
WO2018064591 (A1) |
Neural Networks For Selecting Actions To Be Performed By A Robotic Agent |
WO2018071392 (A1) |
Reinforcement Learning With Auxiliary Tasks |
WO2018083671 (A1) |
Sequence Transduction Neural Networks |
WO2018083670 (A1) |
Recurrent Neural Networks |
WO2018083669 (A1) |
Scene Understanding And Generation Using Neural Networks |
WO2018083668 (A1) |
Reinforcement Learning Systems |
WO2018083667 (A1) |
Training Action Selection Neural Networks |
WO2018083532 (A1) |
Continuous Control With Deep Reinforcement Learning |
MX2018000942 (A) |
Data-Efficient Reinforcement Learning For Continuous Control Tasks |
WO2018142212 (A1) |
Memory Augmented Generative Temporal Models |
WO2018142378 (A1) |
Neural Programming |
EP3360082 (A1) |
Augmenting Neural Networks With External Memory |
KR20180091850 (A) |
Neural Episodic Control |
WO2018154100 (A1) |
Multiscale Image Generation |
WO2018154092 (A1) |
Action Selection For Reinforcement Learning Using Neural Networks |
WO2018153807 (A1) |
Training Machine Learning Models |
WO2018153806 (A1) |
Dueling Deep Neural Networks |
US2018260689 (A1) |
Asynchronous Deep Reinforcement Learning |
US2018260708 (A1) |
Training Neural Networks Using Posterior Sharpening |
WO2018172513 (A1) |
Selecting Action Slates Using Reinforcement Learning |
EP3384435 (A1) |
Distributional Reinforcement Learning |
WO2018189404 (A1) |
Black-Box Optimization Using Neural Networks |
WO2018189279 (A1) |
Generating Images Using Neural Networks |
CN108701249 (A) |
Training Neural Networks Using A Prioritized Experience Memory |
CN108701252 (A) |
Training Neural Networks Using Normalized Target Outputs |
CN108701253 (A) |
Associative Long Short-Term Memory Neural Network Layers |
EP3398118 (A1) |
Compressing Images Using Neural Networks |
EP3398114 (A1) |
Augmenting Neural Networks With External Memory |
EP3398117 (A1) |
Generating Audio Using Neural Networks |
US2018322891 (A1) |
Processing Text Sequences Using Neural Networks |
US2018329897 (A1) |
Spatial Transformer Modules |
US2018330185 (A1) |
Generating Output Examples Using Bit Blocks |
US2018336455 (A1) |
Programmable Reinforcement Learning Systems |
WO2018211146 (A1) |
Making Object-Level Predictions Of The Future State Of A Physical System |
WO2018211144 (A1) |
Neural Network System |
WO2018211143 (A1) |
Imagination-Based Agent Neural Networks |
WO2018211142 (A1) |
Imagination-Based Agent Neural Networks |
WO2018211141 (A1) |
Data Efficient Imitation Of Diverse Behaviors |
WO2018211140 (A1) |
Training Action Selection Neural Networks Using A Differentiable Credit Function |
WO2018211139 (A1) |
Multitask Neural Network Systems |
WO2018211138 (A1) |
Neural Network Systems For Action Recognition In Videos |
WO2018210796 (A1) |
Training Action Selection Neural Networks Using Look-Ahead Search |
WO2018215665 (A1) |
Noisy Neural Network Layers |
WO2018215344 (A1) |
Training Action Selection Neural Networks |
WO2018224695 (A1) |
Generating Discrete Latent Representations Of Input Data Items |
WO2018224690 (A1) |
Selecting Actions Using Multi-Modal Inputs |
WO2018224471 (A1) |
Feedforward Generative Neural Networks |
US2018365554 (A1) |
Generalizable Medical Image Analysis Using Segmentation And Classification |
US10198832 (B2); |
Neural Networks |
US2019005684 (A1) |
Training Action Selection Neural Networks Using Apprenticeship |
WO2019002465 (A1) |
Learning Visual Concepts Using Neural Networks |
WO2019011968 (A1) |
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